In order to improve performance and operating efficiency of electronic devices, it is important to properly analyze and manage their power consumption. As an example, computing devices having a voltage regulator device (VRD) are often assigned variable workloads with little or no regards to the workload's distribution and its impact on the computing device's power consumption. This can be troublesome because the device can then operate using either too little or too much power. Either case results in a waste of resources because the optimal peak power consumption and correspondingly, efficiency, are not being utilized. Such wastes can include lost productivity, operating efficiency, time, and money. This problem is further magnified when a multitude of computing devices are used because those lost resources get compounded. While energy efficiency curves have been used to determine operating power efficiency, such curves are usually created using static sets of data. As such, these curves are unable to help the device stay in peak power efficiency mode because the assigning of workloads onto the device creates dynamic data sets that must be accounted for. Specifically, the addition of workloads onto a device result in power consumption changes that must then be used to recalibrate the curves to ensure that the device is operating in the optimal region. Due to the static nature of the present curves, computing devices cannot and are not being used most efficiently.
In another example, datacenters contain many computing devices, such as servers. Workloads are often distributed en masse to servers with little to no regards to the servers' energy efficiency, much like in the example above. Datacenters generally exhibit a high sensitivity to power consumption because they often need to consume high levels of power to operate the servers. As such, datacenters often rely on energy efficiency curves to determine peak operating efficiency. However, those curves are generated based on static data collected at various usage and corresponding power consumption levels. Yet, as the amount of workload placed onto the server changes, so too does the server's power consumption needs. These curves fail to reflect such changes since they are based on past data. In addition to workload changes, hardware configurations can also change, resulting in a corresponding power consumption change. In either case, the energy efficiency curves relied upon by the datacenters become outdated and can no longer predict optimal peak power consumption and efficiency. To obtain and analyze this dynamic data, cumbersome hardware must be added onto each server, along with a communication interface in order to transmit the data for recalibration of the energy efficiency curves. For a datacenter with thousands upon thousands of servers, this process is extremely costly and tedious. Therefore, implementation of this process is an untenable solution. Another solution is needed that is elegant, simple-to-implement, and cost-effective.